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Computer-Assisted Evaluation of industrial chemical Substances According to Regulations


The general aim of CAESAR is to produce (Q)SAR models for the prediction of the toxicity of chemical substances. These models will be designed to be used for regulatory purposes, more specifically to assist the implementation of the proposed REACH system. The project will develop predictive in silicon models for chemical toxicity. These modelling approaches will reduce the dependence on the use of animals as well as the costs associated with toxicity tests, and will have wide-ranging applications.

The work will involve a robust and detailed characterisation of the most relevant endpoints as defined in EC documents relating to the requirements for REACH. Utmost attention will be paid to obtain a solid experimental basis by the use of toxicity data that have been checked for quality at all stages. At least 5 clearly defined data sets along with suitable statistical techniques will be exploited to form powerful, innovative and thoroughly validated (Q)SAR models. Models developed by each partner will be checked by at least one other partner. Each model will be characterised by its statistical uncertainty. The presence of false negative predictions will also be evaluated to reduce the risk of predicting potentially harmful compounds to be non-toxic. The applicability domain of each (Q)SAR will be described, both in terms of its boundaries and density in chemical and biological space. For each (Q)SAR, a detailed protocol will be prepared, describing the standardised models and the quality procedure adopted.

The (Q)SAR models developed in this project, and their protocols, will be placed on the project's internet site to allow the widest possible free access. Further dissemination of the models will be through an international workshop at the end of the project. Exploitation will be achieved through the involvement of users and regulatory bodies. An external review committee will be formed to monitor the project progress and provide advice.

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Participants (8)